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Persistent Knowledge

Memory That
Never Forgets

Alpha PM builds a knowledge graph of your project. Decisions, context, patterns—all connected and retrievable. Ask about something from 6 months ago.

auth_decision_jwt
api_rate_limit_config
team_standup_summary
🧠
deployment_rollback_jan
customer_feedback_q4
architecture_decision
∞
Context window
<100ms
Memory recall
Months
Of history

Every conversation. Every decision. Every context. Alpha PM doesn't just respond—it remembers. The knowledge compounds over time.

Memory Types

What Gets Remembered

Different types of knowledge, all interconnected

💬

Conversation Memory

Every Slack conversation is indexed and searchable. Reference past discussions instantly.

"What did we decide about caching?" "Show me the auth discussion from March"
🎯

Decision Memory

Technical decisions are stored with rationale, alternatives considered, and stakeholders.

"Why did we choose PostgreSQL?" "What were the Redis alternatives?"
📋

Project Memory

Issue histories, PR patterns, deployment events. The full lifecycle of your project.

"What broke in last week's deploy?" "How did we fix the timeout bug?"
👥

Team Memory

Who knows what, who worked on what, expertise mapping across your team.

"Who's worked on payments before?" "Who reviewed the auth PR?"
In Action

Memory at Work

See how context from months ago surfaces instantly

D
Hey Alpha, I'm looking at the rate limiting code. Why did we go with Redis instead of in-memory?
A
Good question! You discussed this with Marcus back in January. The decision was driven by the multi-instance deployment requirement—in-memory wouldn't share state across pods.
📝 Retrieved Memory
Decision: Rate Limiter Storage
Date: Jan 15, 2024 • Participants: David, Marcus
Choice: Redis over in-memory
Rationale: "Need shared state for k8s multi-pod deployment. In-memory would require sticky sessions which we want to avoid."
D
Oh right! Did we consider any other options?
A
Yes—you also looked at DynamoDB and Memcached. DynamoDB was ruled out due to cost at our request volume. Memcached lost to Redis because we wanted the sorted sets for sliding window implementation.
Use Cases

When Memory Matters

Context that makes AI actually useful

🆕

New Team Members

Onboard instantly with access to all historical context and decisions.

🔄

Revisiting Decisions

Know why something was done before proposing a change.

🐛

Debugging

"Did we change this recently?" answered in seconds.

📊

Pattern Recognition

Spot recurring issues that humans miss over time.

Ready for Persistent Memory?

Memory is a core feature of Alpha PM. Never lose context again.

📋 View the Epic Learn About Alpha PM